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Deep Learning Data Preprocessing

Manage and preprocess data for deep learning

Preprocessing data is a common first step in the deep learning workflow to prepare raw data in a format that the network can accept. For example, you can resize image input to match the size of an image input layer. You can also preprocess data to enhance desired features or reduce artifacts that can bias the network. For example, you can normalize or remove noise from input data.

You can preprocess image input with operations such as resizing by using datastores and functions available in MATLAB® and Deep Learning Toolbox™. Other MATLAB toolboxes offer functions, datastores, and apps for labeling, processing, and augmenting deep learning data. Use specialized tools from other MATLAB toolboxes to process data for domains such as image processing, object detection, semantic segmentation, signal processing, audio processing, and text analytics.

Applications

Image LabelerLabel images for computer vision applications
Video LabelerLabel video for computer vision applications
Ground Truth LabelerLabel ground truth data for automated driving applications
Lidar LabelerLabel ground truth data in lidar point clouds
Signal LabelerLabel signal attributes, regions, and points of interest, and extract features

Rubriques

Preprocess Deep Learning Data

Label Ground Truth Training Data

Customize Datastores